查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on robotics have been pr esented. According to news originating from Fairfax, Virginia, by NewsRx corresp ondents, research stated, “Robots are being utilized in many different ways in t oday’s society.” Financial supporters for this research include George Mason University. The news correspondents obtained a quote from the research from George Mason Uni versity: “Teleoperated robots are robots that can be controlled from a distance. This is commonly done with remote controllers while observing the robot through a screen, but using virtual reality (VR) is also a possibility. VR teleoperatio n has many benefits, but if a robot is operating in a dangerous environment, as is one of the main applications for these types of robots, the feeling of immers ion caused by VR may be problematic. If the operator feels they themselves are i n danger, that may negatively impact their performance. The following paper intr oduces Applied Adaptive VR (AAVR) as a potential solution to this problem. AAVR is a combination of current VR technologies, such as fear modeling, that could b e applied to real world teleoperation tasks.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news originating from JinJu, South Korea, by NewsRx correspondents, research stated, “The antioxidant properties of Ainsli aea acerifolia, a wild edible plant, were examined by ultrasonic-assisted ethano l extraction methods. The primary objective was to optimize the extraction condi tions and accurately predict antioxidant activities using advanced machine learn ing models.” Our news journalists obtained a quote from the research from Gyeongsang National University: “The extraction conditions were optimized using Response Surface Me thodology (RSM). Various parameters, including temperature, extraction time, and ethanol concentration, were adjusted to maximize antioxidant activity. The opti mal conditions identified were a temperature of 68 °C, an extraction time of 86 min, and an ethanol concentration of 57%. Under these conditions, t he extracts exhibited the highest antioxidant activity. To enhance the predictiv e accuracy of antioxidant activity, an XGBoost (XGB) model was employed. The XGB model performance was evaluated and compared with the RSM model. The XGB model achieved an R² value of 94.71%, significantly outperforming the RSM model by 12.8%. This highlights the superiority of the XGB model i n predicting antioxidant activities based on the given extraction parameters.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news reporting originating from Beijing, People’s Re public of China, by NewsRx correspondents, research stated, “Assessing the influ ence of respiratory assistive devices on the diaphragm mobility is essential for advancing patient care and improving treatment outcomes. Existing respiratory a ssistive robots have not yet effectively assessed their impact on diaphragm mobi lity.” Financial support for this research came from National Natural Science Foundatio n of China. Our news editors obtained a quote from the research from China-Japan Friendship Hospital, “In this study, we introduce for the first time a non-invasive, real-t ime clinically feasible ultrasound method to evaluate the impact of soft wearabl e robots on diaphragm displacement. We measured and compared diaphragm displacem ent and lung volume in eight participants during both spontaneous and robotic-as sisted respiration. Building on these measurements, we proposed a human-robot co upled two-compartment respiratory mechanics model that elucidates the underlying mechanism by which our extracorporeal wearable robots augments respiration. Spe cifically, the soft robot applies external compression to the abdominal wall mus cles, inducing their inward movement, which consequently pushes the diaphragm up ward and enhances respiratory function. Finally, we investigated the level and s hape of various robotic assistive forces on diaphragm motion. This robotic inter vention leads to a significant increase in average diaphragm displacement by 1.9 5 times and in lung volume by 2.14 times compared to spontaneous respiration. Fu rthermore, the accuracy of the proposed respiratory mechanics model is confirmed by the experimental results, with less than 7% error in measureme nts of both diaphragm displacement and lung volume. Finally, the magnitude of ro botic assistive forces positively correlates with diaphragm movement, while the shape of the forces shows no significant relationship with diaphragm activity. O ur experimental findings validate the effective assistance mechanism of the prop osed robot, which enhances diaphragm mobility and assists in ventilation through extracorporeal robotic intervention. This robotic system can assist with ventil ation while increasing diaphragm mobility, potentially resolving the issue of di aphragm atrophy.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from West Lafayet te, Indiana, by NewsRx correspondents, research stated, “Training Deep Neural Ne tworks (DNNs) places immense compute requirements on the underlying hardware pla tforms, expending large amounts of time and energy. An important factor contribu ting to the long training times is the increasing dataset complexity required to reach state-of-the-art performance in real-world applications.” Our news correspondents obtained a quote from the research from Purdue Universit y: “To address this challenge, we explore the use of input mixing, where multipl e inputs are combined into a single composite input with an associated composite label for training. The goal is for training on the mixed input to achieve a si milar effect as training separately on each the constituent inputs that it repre sents. This results in a lower number of inputs (or mini-batches) to be processe d in each epoch, proportionally reducing training time. We find that naive input mixing leads to a considerable drop in learning performance and model accuracy due to interference between the forward/backward propagation of the mixed inputs . We propose two strategies to address this challenge and realize training speed ups from input mixing with minimal impact on accuracy. First, we reduce the impa ct of inter-input interference by exploiting the spatial separation between the features of the constituent inputs in the network’s intermediate representations . We also adaptively vary the mixing ratio of constituent inputs based on their loss in previous epochs.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news originating from St. Louis, Missouri, by NewsRx c orrespondents, research stated, “Center dot Context.-More people receive a diagn osis of skin cancer each year in the United States than all other cancers combin ed. Many patients around the globe do not have access to highly trained dermatop athologists, whereas some biopsy diagnoses of patients who do have access result in disagreements between such specialists.” Our news journalists obtained a quote from the research from Washington Universi ty, “Mechanomind has developed software based on a deep-learning algorithm to cl assify 40 different diagnostic dermatopathology entities to improve diagnostic a ccuracy and to enable improvements in turnaround times and effort allocation. Ob jective.-To assess the value of machine learning for microscopic tissue evaluati on in dermatopathology.Design.-A retrospective study comparing diagnoses of hema toxylin and eosin-stained glass slides rendered by 2 senior board-certified path ologists not involved in algo- rithm creation with the machine learning algorith m’s classification was conducted. A total of 300 glass slides (1 slide per patie nt’s case) from 4 hospitals in the United States and Africa with common variatio ns in tissue preparation, staining, and scanning methods were included in the st udy.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news originating from Vadodara, India, by NewsRx corresp ondents, research stated, “Deep learning models have overcome traditional machin e learning techniques for text classification domains in the field of natural la nguage processing (NLP). Since, NLP is a branch of machine learning, used for in terpreting language, classifying text of interest, and the same can be applied t o analyse the medical clinical electronic health records.” Our news journalists obtained a quote from the research from the Faculty of Engi neering and Technology, “Medical text consists of lot of rich data which can alt ogether provide a good insight, by determining patterns from the clinical text d ata. In this paper, bidirectional-long short-term memory (Bi-LSTM), bi-LSTM atte ntion and bidirectional encoder representations from transformers (BERT) base mo dels are used to classify the text which are of privacy concern to a person and which should be extracted and can be tagged as sensitive. This text data which w e might think not of privacy concern would majorly reveal a lot about the patien t’s integrity and personal life. Clinical data not only have patient demographic data but lot of hidden data which might go unseen and thus could arise privacy issues. Bi-LSTM with attention layer is also added on top to realize the importa nce of critical words which will be of great importance in terms of classificati on, we are able to achieve accuracy of about 92%. About 206,926 sen tences are used out of which 80% are used for training and rest fo r testing we get accuracy of 90% approx. with Bi-LSTM alone.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news originating from Toronto , Canada, by NewsRx correspondents, research stated, “Canadian Institute for Hea lth Information (CIHI) is looking to modernize and adopt new ways of working.” The news editors obtained a quote from the research from Canadian Institute for Health Information (CIHI): “This incudes the use of new technology, including th e application of Artificial Intelligence (AI). To begin in a purposeful manner, the organization developed an AI strategy which was informed through feedback fr om key stakeholders and partners, from its staff and from a review of internatio nal research.” According to the news editors, the research concluded: “The research informed se veral ways AI could add value to CIHI’s internal operations and to the external role CIHI could play in advancing responsible AI adoption in health systems acro ss Canada. This article describes the strategy development process and the areas of focus within the strategy.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Pericarditis is the subject of a report. According to news reporting ou t of Cleveland, Ohio, by NewsRx editors, research stated, “Recurrent pericarditi s (RP) is a complex condition associated with significant morbidity. Prior studi es have evaluated which variables are associated with clinical remission.” Our news journalists obtained a quote from the research from Vascular and Thorac ic Institute, “However, there is currently no established risk-stratification mo del for predicting outcomes in these patients. We developed a risk stratificatio n model that can predict long-term outcomes in patients with RP and enable ident ification of patients with characteristics that portend poor outcomes. We retros pectively studied a total of 365 consecutive patients with RP from 2012 to 2019. The primary outcome was clinical remission (CR), defined as cessation of all an ti-inflammatory therapy with complete resolution of symptoms. Five machine learn ing survival models were used to calculate the likelihood of CR within 5 years a nd stratify patients into high-risk, intermediate-risk, and low-risk groups. Amo ng the cohort, the mean age was 46 ± 15 years, and 205 (56%) were w omen. CR was achieved in 118 (32%) patients. The final model includ ed steroid dependency, total number of recurrences, pericardial late gadolinium enhancement, age, etiology, sex, ejection fraction, and heart rate as the most i mportant parameters. The model predicted the outcome with a C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients i nto low-risk, intermediate-risk, and high-risk groups (log-rank test; P<0.0001). We developed a novel risk-stratification model for predicting CR in RP . Our model can also aid in stratifying patients, with high discriminative abili ty.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Lacombe, Can ada, by NewsRx correspondents, research stated, “Machine learning classification approaches were used to discriminate a fishy off-flavour identified in beef wit h health-enhanced fatty acid profiles. The random forest approach outperformed ( P <0.001; receiver operating characteristic curve: 99.8 % , sensitivity: 99.9 % and specificity: 93.7 %) the lo gistic regression, partial least-squares discrimination analysis and the support vector machine (linear and radial) approaches, correctly classifying 100 % and 82 % of the fishy and non-fishy meat samples, respectively.” Our news editors obtained a quote from the research from Lacombe Research and De velopment Centre, “The random forest algorithm identified 20 volatile compounds responsible for the discrimination of fishy from non-fishy meat samples. Among t hose, seven volatile compounds (pentadecane, octadecane, gdodecalactone, dodeca nal, (E,E)-2,4-heptadienal, 2-heptanone, and ethylbenzene) were selected as sign ificant contributors to the fishy off-flavour fingerprint, all being related to lipid oxidation.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Zhejiang, Peopl e’s Republic of China, by NewsRx editors, research stated, “This study aims to a ssess how enhancing upper limb function on the affected side of stroke influence s the gait of the lower limb. Forty eligible stroke patients were randomly assig ned to either a control group or a treatment group, with 20 patients in each gro up.” Our news journalists obtained a quote from the research from the First Affiliate d Hospital of Zhejiang Chinese Medical University, “Both groups underwent dynami c evaluation using artificial intelligence and computer vision before treatment. This evaluation focused on analyzing the range of motion of the shoulder and el bow during the gait cycle, as well as various gait parameters (such as step leng th, step speed, and percentage of stance phase) on the affected side. Following evaluation, the control group received routine rehabilitation treatment. The res ults indicated that there was no significant difference between the two groups b efore treatment. However, following treatment, there was a notable improvement i n the motion of the shoulder and elbow joints on the affected side among patient s in the treatment group (p <0.05), whereas the control gro up showed only slight improvement, which was not statistically significant (p > 0.05). The improvement in upper limb function on the affected side also appears to positively influence gait recovery. However, it’s important to note that the observation period was relatively short.”
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